Hemodynamic analysis of optimal branch selection for flow-diverting stents in middle cerebral artery bifurcation aneurysms: a pilot study.

Background: Despite the expanding use of flow-diverting stents (flow diverters-FDs) for middle cerebral artery (MCA) bifurcation aneurysms, their efficacy remains contentious in this complex anatomy. Current studies report conflicting complete occlusion rates (55-92%) and significant branch stenosis/occlusion risks (8-43%), highlighting unmet needs in stent placement strategy.

Methods: In this retrospective cohort study combining clinical data with computational fluid dynamics (CFD), 20 MCA bifurcation aneurysms (19 patients) treated with FDs were analyzed.

SarAdapter: Prioritizing Attention on Semantic-Aware Representative Tokens for Enhanced Medical Image Segmentation.

Transformer-based segmentation methods exhibit considerable potential in medical image analysis. However, their improved performance often comes with increased computational complexity, limiting their application in resource-constrained medical settings. Prior methods follow two independent tracks: (i) accelerating existing networks via semantic-aware routing, and (ii) optimizing token adapter design to enhance network performance.

Investigation of early-stage cardiovascular-kidney-metabolic syndrome on retinal structure and function evaluated by optical coherence tomography angiography.

Background: The retina serves as a non-invasive window to visualize systemic health, with optical coherence tomography angiography (OCTA) enabling simultaneous assessment of retinal structural thickness and vascular density. Suboptimal cardiovascular-kidney-metabolic (CKM) health status, defined by the early stage of CKM syndrome, may impact retinal neurovascular integrity, yet comprehensive OCTA-based evaluations remain limited and controversial. This study aimed to investigate retinal structural and vascular alterations in the early-stage CKM using OCTA and explore its potential as a biomarker tool.

MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition From Fundus Images.

Existing multi-modal learning methods on fundus and OCT images mostly require both modalities to be available and strictly paired for training and testing, which appears less practical in clinical scenarios. To expand the scope of clinical applications, we formulate a novel setting, "OCT-enhanced disease recognition from fundus images", that allows for the use of unpaired multi-modal data during the training phase, and relies on the widespread fundus photographs for testing. To benchmark this setting, we present the first large multi-modal multi-class dataset for eye disease diagnosis, MultiEYE, and propose an OCT-assisted Conceptual Distillation Approach (OCT-CoDA), which employs semantically rich concepts to extract disease-related knowledge from OCT images and leverages them into the fundus model.

A Deep Learning Network for Accurate Retinal Multidisease Diagnosis Using Multiview Fusion of En Face and B-Scan Images: A Multicenter Study.

Purpose: Accurate diagnosis of retinal disease based on optical coherence tomography (OCT) requires scrutiny of both B-scan and en face images. The aim of this study was to investigate the effectiveness of fusing en face and B-scan images for better diagnostic performance of deep learning models.

Methods: A multiview fusion network (MVFN) with a decision fusion module to integrate fast-axis and slow-axis B-scans and en face information was proposed and compared with five state-of-the-art methods: a model using B-scans, a model using en face imaging, a model using three-dimensional volume, and two other relevant methods.

Vessel-promoted OCT to OCTA image translation by heuristic contextual constraints.

Optical Coherence Tomography Angiography (OCTA) is a crucial tool in the clinical screening of retinal diseases, allowing for accurate 3D imaging of blood vessels through non-invasive scanning. However, the hardware-based approach for acquiring OCTA images presents challenges due to the need for specialized sensors and expensive devices. In this paper, we introduce a novel method called TransPro, which can translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images without requiring any additional hardware modifications.

Synthesizing Feature-Aligned and Category-Aware Electronic Medical Records for Intracranial Aneurysm Rupture Prediction.

Rupture prediction is crucial for precise treatment and follow-up management of patients with intracranial aneurysms (IAs). Considerable machine learning (ML) methods have been proposed to improve rupture prediction by leveraging electronic medical records (EMRs), however, data scarcity and category imbalance strongly influence performance. Thus, we propose a novel data synthesis method i.

Prediction of Visual Outcome After Rhegmatogenous Retinal Detachment Surgery Using Artificial Intelligence Techniques.

Purpose: This study aimed to develop artificial intelligence models for predicting postoperative functional outcomes in patients with rhegmatogenous retinal detachment (RRD).

Methods: A retrospective review and data extraction were conducted on 184 patients diagnosed with RRD who underwent pars plana vitrectomy (PPV) and gas tamponade. The primary outcome was the best-corrected visual acuity (BCVA) at three months after the surgery.

Flow diverters treatment planning of small- and medium-sized intracranial saccular aneurysms on the internal carotid artery via constraint-based virtual deployment.

Purpose: The internal carotid artery (ICA) is a region with a high incidence for small- and medium-sized saccular aneurysms. However, the treatment relies heavily on the surgeon's experience to achieve optimal outcome. Although the finite element method (FEM) and computational fluid dynamics can predict the postoperative outcomes, due to the computational complexity of traditional methods, there is an urgent need for investigating the fast but versatile approaches related to numerical simulations of flow diverters (FDs) deployment coupled with the hemodynamic analysis to determine the treatment plan.

GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality.

Cellular energy supply for promoting vascular remodeling of small-diameter vascular grafts: a preliminary study of a new strategy for vascular graft development.

Rapid endothelialization is extremely essential for the success of small-diameter tissue-engineered vascular graft (TEVG) (<6 mm) transplantation. However, severe inflammation often causes cellular energy decline of endothelial cells. The cellular energy supply involved in vascular graft therapy remains unclear, and whether promoting energy supply would be helpful in the regeneration of vascular grafts needs to be established.

A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata.

Introduction: Skin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion. We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones.

A deep learning-based automatic system for intracranial aneurysms diagnosis on three-dimensional digital subtraction angiographic images.

Background: Intracranial aneurysms (IAs) are a life-threatening disease. Their rupture can lead to hemorrhagic stroke. Most studies applying deep learning for the detection of aneurysms are based on angiographic images.

Evaluation of aneurysm rupture risk based upon flowrate-independent hemodynamic parameters: a multi-center pilot study.

Background: Specifying generic flow boundary conditions in aneurysm hemodynamic simulations yields a great degree of uncertainty for the evaluation of aneurysm rupture risk. Herein, we proposed the use of flowrate-independent parameters in discriminating unstable aneurysms and compared their prognostic performance against that of conventional absolute parameters.

Methods: This retrospective study included 186 aneurysms collected from three international centers, with the stable aneurysms having a minimum follow-up period of 24 months.

Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction.

Objectives: We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data.

Method: Using segmented aneurysm mask as input, a backbone model was pretrained using a self-supervised method to learn deep embeddings of aneurysm morphology from 947 unlabeled cases of angiographic images. Subsequently, the backbone model was finetuned using 120 labeled cases with known rupture status.

Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study.

The prediction of aneurysm treatment outcomes can help to optimize the treatment strategies. Machine learning (ML) has shown positive results in many clinical areas. However, the development of such models requires expertise in ML, which is not an easy task for surgeons.

Flow diverter modeled as heterogeneous and anisotropic porous medium: Simulation, experimental validation and case analysis.

Simulation of flow diverter (FD) treated aneurysm can evaluate treatment efficacy and aid treatment planning. However, explicit modeling of thin wires of FD impose extremely high demand of computational resources and time, which limit its use in time-sensitive presurgical planning. One alternative approach is to model FD as homogenous porous medium, which saves time but with compromise in accuracy.

Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data.

Assessment of cerebral aneurysm rupture risk is an important task, but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data, and lack of interpretability may limit their use in clinical setting.

A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms.

Objectives: Prediction of intracranial aneurysm rupture is important in the management of unruptured aneurysms. The application of radiomics in predicting aneurysm rupture remained largely unexplored. This study aims to evaluate the radiomics differences between ruptured and unruptured aneurysms and explore its potential use in predicting aneurysm rupture.

Elevated Lipid Infiltration Is Associated With Cerebral Aneurysm Rupture.

Intracranial aneurysm wall degradation can be associated with lipid infiltration. However, the relationship between lipid infiltration and aneurysm rupture has not been explored quantitatively. To investigate the correlation between lipid infiltration and aneurysm rupture, we utilized patient-specific simulation of low-density lipoprotein (LDL) transport to analyze lipid infiltration in the cerebral aneurysm wall.

Deep learning for automated cerebral aneurysm detection on computed tomography images.

Purpose: Cerebrovascular aneurysms are being observed with rapidly increasing incidence. Therefore, tools are needed for accurate and efficient detection of aneurysms. We used deep learning techniques with CT angiography acquired from multiple medical centers and different machines to develop and evaluate an automatic detection model.

Morphological and Hemodynamic Differences Between Aneurysmal Middle Cerebral Artery Bifurcation and Contralateral Nonaneurysmal Anatomy.

Background: The morphological and hemodynamic features differ between middle cerebral artery (MCA) bifurcations with and without aneurysms.

Objective: To investigate the morphological and hemodynamic differences between aneurysmal MCA bifurcation and contralateral nonaneurysmal anatomy.

Methods: Computed tomography angiography of 36 patients with unilateral small saccular MCA bifurcation aneurysms was evaluated.

Hemodynamic modeling of leukocyte and erythrocyte transport and interactions in intracranial aneurysms by a multiphase approach.

Hemodynamics has been recognized as an important factor in the development, growth, and rupture of cerebral aneurysms, and investigated by computational fluid dynamics techniques using a single phase approach. However, flow-dependent cell transport and interactions are usually ignored in single phase models, in which blood is usually treated as a single phase Newtonian fluid. For getting better insight into the underlying pathology of intracranial aneurysm, cell transport and interactions should be covered in hemodynamic studies.

A computational model based on fibrin accumulation for the prediction of stasis thrombosis following flow-diverting treatment in cerebral aneurysms.

Flow diverters, the specially designed low porosity stents, have been used to redirect blood flow from entering aneurysm, which induces flow stasis in aneurysm and promote thrombosis for repairing aneurysm. However, it is not clear how thrombus develops following flow-diversion treatment. Our objective was to develop a computation model for the prediction of stasis-induced thrombosis following flow-diversion treatment in cerebral aneurysms.